Attributes Extraction for Fine-grained Differentiation of the Internet of Things Patterns

Sithole, Vusi; Marshall, Linda

doi:10.1145/3351108.3351118

Cited by 2 publications

(3 citation statements)

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“…In this section we present a multifaceted approach for mining knowledge graphs to map heterogeneous relationships between the IoT patterns. In the literature, there are several methods used to organise the IoT patterns, including but not limited to: i) organising patterns by their scope and purpose [11]; ii) organising patterns by their semantics or properties [15]; iii) organising patterns by their design level scalability [8]; iv) organising patterns by their relationships [16]. The focus of this paper is on an approach for organising the IoT patterns by their heterogeneous relationships.…”

Section: The Proposed Procedures For Mining Knowledge Graphs To Map the Relations Between The Iot Patternsmentioning

confidence: 99%

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Mining knowledge graphs to map heterogeneous relations between the internet of things patterns

Sithole

Marshall

2021

IJECE

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<span lang="EN-US">Patterns for the internet of things (IoT) which represent proven solutions used to solve design problems in the IoT are numerous. Similar to object-oriented design patterns, these IoT patterns contain multiple mutual heterogeneous relationships. However, these pattern relationships are hidden and virtually unidentified in most documents. In this paper, we use machine learning techniques to automatically mine knowledge graphs to map these relationships between several IoT patterns. The end result is a semantic knowledge graph database which outlines patterns as vertices and their relations as edges. We have identified four main relationships between the IoT patterns-a pattern is similar to another pattern if it addresses the same use case problem, a large-scale pattern uses a small- scale pattern in a lower level layer, a large pattern is composed of multiple smaller scale patterns underneath it, and patterns complement and combine with each other to resolve a given use case problem. Our results show some promising prospects towards the use of machine learning techniques to generate an automated repository to organise the IoT patterns, which are usually extracted at various levels of abstraction and granularity.</span>

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Section: The Proposed Procedures For Mining Knowledge Graphs To Map the Relations Between The Iot Patternsmentioning

confidence: 99%

“…The function (15) helps us to determine the degree to which two given features rate the same set of sentences. In Table 6, the symbol * marks those features that are significantly correlated.…”

Section: Correlations Between Featuresmentioning

confidence: 99%

Mining knowledge graphs to map heterogeneous relations between the internet of things patterns

Sithole

Marshall

2021

IJECE

Self Cite

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“…Vega-Barbas et al [36] focus on the human-related aspects of IoT in smart spaces and define five interaction patterns that aim to capture the "good manners" of user interaction in IoT. Sithole and Marchall [37] present an exciting work on the attributes extraction for a fine-grained description and differentiation of the IoT patterns. This approach aims to provide an insight into IoT patterns with the objective to quickly and efficiently differentiate them based on various aspects.…”

Section: Related Workmentioning

confidence: 99%

An Overview of Design Patterns for Self-Adaptive Systems in the Context of the Internet of Things

et al. 2020

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The Internet of Things (IoT) requires the integration of all available, highly specialized, and heterogeneous devices, ranging from embedded sensor nodes to servers in the cloud. The self-adaptive research domain provides adaptive capabilities that can support the integration in IoT systems. However, developing such systems is a challenging, error-prone, and time-consuming task. In this context, design patterns propose already used and optimized solutions to specific problems in various contexts. Applying design patterns might help to reuse existing knowledge about similar development issues. However, so far, there is a lack of taxonomies on design patterns for self-adaptive systems. To tackle this issue, in this paper, we provide a taxonomy on design patterns for self-adaptive systems that can be transferred to support adaptivity in IoT systems. Besides describing the taxonomy and the design patterns, we discuss their applicability in an Industrial IoT case study.

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Attributes Extraction for Fine-grained Differentiation of the Internet of Things Patterns

Cited by 2 publications

References 10 publications

Mining knowledge graphs to map heterogeneous relations between the internet of things patterns

Mining knowledge graphs to map heterogeneous relations between the internet of things patterns

An Overview of Design Patterns for Self-Adaptive Systems in the Context of the Internet of Things

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